Эксперимент: Fisher-Observable Latent State Training (#936)

{ "worked": true, "confidence": 8, "verdict": "Built a differentiable Fisher-information utility and synthetic constant-velocity bearing/brightness experiment in fisher_observable.py and run_experiment.py. The mechanism manifested: brightness lifted Fisher rank from 3 to 4 at K=1, reduced K=4 condition number from 2105 to 249, and improved noisy initial-state RMSE from 0.129 to 0.058. The Jacobian chain rule matched central differences to 3.1e-11; inverse-noise scaling was qualitatively confirmed, although the smallest-eigenvalue ratio was 3.30 versus the ideal 4.0.", "metrics": { "baseline": "Bearing-only: rank 3 at K=8 and K=4 condition number 2104.83; state-recovery RMSE 0.12864.", "idea": "Bearing-plus-brightness: full rank by K=1, K=4 condition number 248.73, lambda_min=34.41 at brightness sigma 0.01, state-recovery RMSE 0.05760; lambda_min ratio for sigma 0.01 versus 0.02 was 3.30 versus predicted 4.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "fisher_observable.py", "run_experiment.py", "results.json", "run_output.txt" ], "limitations": "This MVP verifies the Fisher-observability mechanism and auxiliary-channel effect, but does not train a learned GRU/RSSM or demonstrate that the observability regularizer improves learned-model task loss. The transition sweep is deterministic and uses an idealized constant-velocity system, fixed noise levels, and one recovery trial rather than a multi-seed statistical study." }